Date of Award
2020
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Mechanical Engineering
First Advisor
Hamid Ossareh
Second Advisor
James Bagrow
Abstract
In this work, a novel combination of Reference Governors (RG) and Iterative Learning Control (ILC) to address the issue of simultaneous learning and constraint management in systems that perform a task repeatedly is proposed. The proposed control strategy leverages the measured output from the previous iterations to improve tracking, while guaranteeing constraint satisfaction during the learning process.
To achieve this, the plant is modeled by a linear system with uncertainties. An RG solution based on a robust Maximal Admissible Set (MAS) is proposed that endows the ILC algorithm with constraint management capabilities. The proposed method is applied to the Scalar Reference Governor (SRG), the Vector Reference Governor (VRG) and the Command Governor (CG). An update law on the MAS is proposed to further improve performance.
Language
en
Number of Pages
66 p.
Recommended Citation
Laracy, Aidan Ridner, "The Iteration Domain Reference Governor, a Constraint Management Scheme for Batch Processes" (2020). Graduate College Dissertations and Theses. 1259.
https://scholarworks.uvm.edu/graddis/1259